Abstract
Up until this point, we have spoken only loosely of displaying network graphs, although we have shown several examples already. Here in this chapter we consider the problem of display in its own right. Techniques for displaying network graphs are the focus of the field of graph drawing or graph visualization. Such techniques typically seek to incorporate a combination of elements from mathematics, human aesthetics, and algorithms.
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Notes
- 1.
Here and throughout we use terms like ‘draw’ only in the colloquial sense, although more formal mathematical treatments of this topic area exist (e.g., see Chap. 8 of Gross and Yellen [9]) which attach more specialized understandings to these terms.
- 2.
Original source: http://observatoire-presidentielle.fr/. The subnetwork used here is part of the mixer package in R. Note that the inherent directionality of blogs are ignored in these data, as the network graph is undirected.
- 3.
While the DrL algorithm has been found to scale successfully to networks of over 1 million vertices, it is known to produce less than satisfactory results for networks with only hundreds of vertices. Our use of a smaller network here is primarily for the purpose of illustration.
- 4.
See Sect. 4.4.
References
K. Boyack, R. Klavans, and K. Börner, “Mapping the backbone of Science,” Scientometrics, vol. 64, no. 3, pp. 351–374, 2005.
W. Cleveland, The Elements of Graphing Data. Wadsworth, 1985.
W. Cleveland, Visualizing Data. Hobart Press, 1993.
G. Davidson, B. Wylie, and K. Boyack, “Cluster stability and the use of noise in interpretation of clustering,” IEEE Symposium on Information Visualization, pp. 23–30, 2002. 5
G. di Battista, P. Eades, R. Tamassia, and I. Tollis, Graph Drawing. Englewood Cliffs, NJ: Prentice Hall, 1999.
T. Fruchterman and E. Reingold, “Graph drawing by force-directed placement,” Software–Practice and Experience, vol. 21, no. 11, pp. 1129–1164, 1991.
S. Gopal, “The evolving social geography of blogs,” in Societies and Cities in the Age of Instant Access, H. Miller, Ed. Berlin: Springer, 2007, pp. 275–294.
R. Grant, Data Visualization: Charts, Maps, and Interactive Graphics. Chapman and Hall/CRC, 2018.
J. Gross and J. Yellen, Graph Theory And Its Applications. Boca Raton, FL: Chapman & Hall/CRC, 1999.
T. Kamada and S. Kawai, “An algorithm for drawing general undirected graphs,” Information Processing Letters, vol. 31, no. 1, pp. 7–15, 1989.
M. Kaufmann and D. Wagner, Eds., Drawing Graphs. Berlin: Springer, 1998.
E. Kolaczyk, Statistical Analysis of Network Data: Methods and Models. Springer Verlag, 2009.
S. Martin, W. Brown, R. Klavans, and K. Boyack, “DrL: Distributed recursive (graph) layout,” SAND Reports, Tech. Rep. 2936, 2008.
E. Tufte, The Visual Display of Quantitative Information. Graphics Press, 1983.
J. Tukey, Exploratory Data Analysis. New York: Addison-Wesley, 1977.
W. Zachary, “An information flow model for conflict and fission in small groups,” Journal of Anthropological Research, vol. 33, no. 4, pp. 452–473, 1977.
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Kolaczyk, E.D., Csárdi, G. (2020). Visualizing Network Data. In: Statistical Analysis of Network Data with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-44129-6_3
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